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We just had a very successful all-in spirited full contact debate in front of 1,700 people. A packed house. There were
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no open seats. Is the power of the all-in brand is really to have the conversations with a little bit of fun,
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a little bit of spiciness and no question, no topic can be banned. There was no censorship.
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No censorship. We're going to just go right after the hardest topics. But we
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had a really fun time. Discussed so many important topics. What a great panel. These guys are tip of the spear in terms
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of doing really exciting things in business. >> Thanks for coming out everybody. Uh
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we're going to have a great super hardcore discussion about the future specifically around AI which I think is
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the most important theme not only of uh CES 2026 as we've seen with all the incredible gadgets chips being launched
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self-driving but it's going to be the most important transformation of our lifetimes. I think everything we've seen
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over the last 30 years of technology from the PC revolution to cloud computing to the internet, mobile, all
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of that is going to be dwarfed in comparison to the impact that AI is going to have on society. If you're here
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at CES, you know that you're here for that reason. And we've got two amazing
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guests who are going to join us to have this debate. And additionally, I've brought my box, a box filled with all
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the ghosts and gadgets of Christmas past. And we're going to go through those uh at the end of our discussion.
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But here's a quick video of our guests who will be joining me today. >> From boardrooms to the White House and
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beyond, Mackenzie's influence in business is virtually unparalleled. >> It's one of the largest and most
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influential consulting firms in the world. enterprise can move faster than any of us expected, which is good news
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because these problems aren't going to be the problems of the next generation.
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They're going to be the problems of our leadership generation. Making this system of government better on both
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efficiency and effectiveness is key for economic growth and for national defense. We also think that some of the
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private sector insights that we have brought to the public sector can drive innovation. Our next guest leads venture
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capital firm General Catalyst >> with 40 billion in assets under management as of midyear.
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>> Our aspirations in venture capital is to be the best seed firm in the world. The
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decisions we're making, the companies we're building are going to impact the
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world for centuries to come. >> Ladies and gentlemen, please welcome Bob Sternfelds and Hamont Tasia.
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>> All right, gentlemen. Welcome. Yeah. All right team. Let's do it. Let's do it.
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>> How do you look at the pace of innovation and change in this past two years since Chat GPT was launched
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compared to the first 30 years of our careers. We're all of a certain Gen X age. Compare the last two or three years
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to the 30 before it. >> Yeah. Yeah. Well, first Jason, thanks. And it's great to be up here with you
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guys. And uh yeah, I would just say this week is amazing. I I um think there's
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over 150,000 folks here this week. And you talk about CES being back. I think CES is back. And uh and that's great,
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right? And uh >> and with all of these things happening, I think there is such a premium on folks
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from different perspectives getting together because that's where new ideas are created. And and my big hope and why
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we're here is when you mix and mingle with different folks, you come up with
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new things and the world needs new things and you and what I love is you mentioned a lot of the the tech leaders.
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What's exciting for this is I think everybody sees tech as part of the equation. And and so when I look at the
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folks here in CES, you see not only the technology leaders, the investors, but you see folks from almost every industry
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vertical that are here now because they know that technology doesn't sit on the
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side, it's central to everything we do. And I get to your question, look, I think we're moving at at literally warp
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speed now. It's just night and day different. It's almost a, you know, BC A
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type of thing when you can see the change of pace. And I haven't met a CEO yet that isn't talking about how do I
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get my organization moving faster. It's quite frankly less about strategy. It's
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more about organizational speed. Hey Mont, how does this feel compared to our first, you know, couple of decades where
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companies would take two or three years to release a product and now companies are releasing products in two or three
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weeks, two or three months. >> Yeah. So look, the world has completely changed, right? We we've often said this
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is peak ambiguity. You have massive geopolitical change. You have an incredible amount of change around every
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country trying to drive strategic autonomy in different industries. And all those dynamics keep changing.
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Alliances, the new world order, everything. And then underneath that, our tool of implementation is technology
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that keeps changing, right? So what the the technologies can build today versus what the LLM could do, let's say, two
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years ago or November 22, let's say, when Chad GBD came about is fundamentally different. So what are you
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building towards as to what the world's going to look like so you can have enduring value? And then what are you
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building with where the technologies you're using aren't going to obsoles and
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and destroy your value proposition over time. It's just all kinds of change. And
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so it's really dynamic time. And the other thing you will see is you know we
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invested in Stripe in 2010. It became a you know a hundred billion dollar company let's say 12 13 years later. You
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look at Antropic, which we're also investors in, that goes from $60 billion last year to, you know,
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a couple hundred billion. So like, and by the way, with good economic uh progress, these are not pie in the sky
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valuations. They're based on actual growth of the business. Well, that goes back to your point, which is the
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compression of how fast value can create when code self-writes and access to distributions change. So fundamentally,
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it's just it's just really exciting and I think it's going to accelerate from
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here. This was one of the statistics we would look at in venture capital. Hey, how long does it take this company to
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get to a hundred million in revenue? How long does it take to get to a billion in
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revenue? Unpack anthropic and that journey because this company's revenue and you have open AI obviously they're
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contemporary trending towards $20 billion in revenue a year. Where's anthropic at and what's the revenue mix?
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Where does the revenue come from? Well, well, look, so Enthropic builds u language models. It's got one of some of
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the best models out there. There's a couple of companies that are doing a good job at that. And then they've got
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cloud on top, which is to me the essence of transforming the engineering department of enterprise, right? And
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that's a killer application where everybody is now using these tools. So that business when we invested was doing
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about $880 million, which was a 10x growth from the year before, >> 10x year,
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>> 10x growth the year before. and and then and this last year they've announced
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this they're growing another 10x or more and so when you look at that and we
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invested uh at the $60 billion valuation assuming it's going to be like 3x growth
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from there because those are staggering numbers and it does 10x can't predict it
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uh but to see adoption is so fast and so you look at that and say so we ended up
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investing at you know um a 8910 billion kind of run rate business at 60 billion that's the cheapest deal that got done
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last year in venture capital on financial spaces. So, so we just have to get our head around what does scale
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really mean and what you know are we in the business of creating what we used to
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think was like can we create decoorns now we're talking about can we create trillion dollar companies right I mean
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that's not a pie in the sky idea with anthropic and uh open AI and a couple others game changed scale of technology
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is uh you know fundamentally different in what it can do >> Bob what's behind this massive revenue
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ramp because you get to see all the incumbent businesses you get see the elite businesses that are growing, you
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know, two or three times X each year. You also get to see the ones that are struggling and then you see these large
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numbers and 10x your growth. What what's driving this in your mind and is it
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sustainable? I mean, that's the other question. I hate to give you the the classic consultant answer, but I you
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know, I I do think it depends. And I I think we're at a we're at a we're at a
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tipping point this year. And I'll tell you why. I think what's underpinning
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this 10x and 10x you know we work with most of the large enterprise in the world um across all industry verticals
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and what we have seen is a huge uptake of leveraging these technologies like anthropic we're we're we're leveraging
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anthropic and so large enterprise is using technology at a scale and rate that they haven't before and if you look
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at IT spend as a percent of revenue etc all this stuff has gone up and I think that is propelling the 10x to 10x um The
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conundrum is, and it's, you know, been widely written about, um, realizing enterprise at scale value in
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non-technology companies is proving harder than people think. >> Got it. So, in plain English, that
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means, hey, you've got a travel company. There's somebody deploying AI and you're
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watching what's happening at Tesla or Google, and they're getting these phenomenal results, but maybe that
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legacy business is having a harder time achieving those results. I'll make it
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even simpler. Uh typical non- tech CEO might say, "Hey, Bob, do I listen to my
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CFO or my CIO right now, >> right?" CFO is saying, "We've spent all
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this money. Why do we need to be the fast adopter? I'm not seeing the ROI yet. Can we pause?" CIO is saying, "Are
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you freaking crazy? This is the moment that if we don't, we'll be disrupted."
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We think, now I will say the the shining part is I think there's a path where you
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bring those two together as allies. and you say, "Yeah, but let's rethink this,
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get out of pilot purgatory, really think the reorganization, all this stuff."
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There is a path, but I think right now most CEOs are getting torn a bit between, "Do I listen to my CFO or do I
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listen to my CIO?" And I think this is a really good jump off point, Hamont, of
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your strategy at General Catalyst. You and I have known each other for a long time. You really a long time, decades,
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and you always prided yourself on being the great seed fund. We're gonna we're
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going to get to these companies when they're $10 million and 10 people and put that first check in. But then I saw
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this news item go by a month ago that you raised N billion and then I see you're buying companies. So are you out
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of the seed business and now doing random acts of private equity? What's going on here?
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>> Explain to me the strategy and general catalyst. >> How much time do we have? It's going to
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take some time. So, so look, I would say um uh we very much view ourselves as a venture capital for Americ
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for the 25 years we've been around has been meeting founders where they are.
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And what that means is essentially help them navigate ambiguity in the past when
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they're sort of beginning and the business isn't clear uh all the way to
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figuring out how to scale in the complex markets that they go into. So everything
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we've done has been in that context of creating these catalysts the flexible
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capital they need the policy capabilities they need the market access they need uh and those sort of
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relationships globally to actually build an enduring company. So that hasn't
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changed. So So why did we go um you know acquire a health system in u uh Ohio was
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a nonprofit. We worked with the attorney general converted it. And I say this by
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the way with a great sense of responsibility because that's a community of uh in Akran Ohio that we
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take care of. So that hospital has to continue operations in all the dimensions. It takes care of um with
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people. We bought it to actually have a place where we can work with our founders and transform with AI create
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abundance and resilience for this health system so we can take care of the people
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a lot better. Okay. And if we if we did that then we can go do that for the other hundreds of systems across the
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country and be able to do that. So some of it is that's market access. It's very
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hard for healthcare startups to go deploy successfully at scale in these systems. We are going to go show how.
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we're gonna actually go on the ground with do with them and show what the world how so it can transform the health
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system. The you know your other point about uh sort of buying uh companies we look at that as there's a lot of
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workforce transformation happening. Bob and I talk a lot about this. I think work fundamentally in these companies is
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going to change. So if you're a call center in uh an emerging country today the a declining asset value because you
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know it's going to be place displaced with AI. So we look at that and say well
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but those are customers on the other side. If we bought that as a piece of the puzzle to work with an early stage
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founder to learn how to quickly accelerate adoption of AI into uh the call center space and serve these
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customers and scale a lot faster. The compressed value creation we're talking
00:13:00
about that is uh that is a new playbook. So this is not about trying to be PE. This is about acquiring uh uh businesses
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in PE that actually have declining value but have important customers that need to be served and help them get to that
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AI transformation that Bob's talking about faster by getting our founders in
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there. This is extraordinary Bob when you think about it just so the audience can put their head around this venture
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capitalist used to back founders to then be the barbarians at the gate to try to
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take on these big industries. Now these big industries in some cases are in significant decline struggling and the
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venture capitalists are coming in and saying we'll just buy the castle, open
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the draw bridge. We're going to buy it so that we can take our startups and accelerate whether it's healthcare,
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financial services or customer support uh and outsourcing, business process outsourcing. And essentially we don't we
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don't care about that business economically necessarily as much as we care about it for access to that
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customer base running McKenzie. This is a playbook that is like coming out of the future in
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a time capsule and saying we're going to just upend the entire ecosystem. Yeah.
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>> Yeah. I mean, it's I I just gave a talk at a um a university and uh was was
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talking to some potential folks to to join us and I said, "Look, um I'm
00:14:26
jealous. I'm jealous of all of you because you have a lot more time to do what we do than I do. And you're doing
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it at a time where it's going to be a lot more exciting because if you just link this and hey, what I love is um it
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effectively you're creating a new asset class, right? This is not private equity. This is about how do you
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transform incumbent entities into something different, right? Private equity typically optimizes an existing
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asset class at a certain scale. This is about transformation. So you think of large existing enterprise and I think
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you have a choice. You have a choice of transform or die. >> And so there's this wonderful moment and
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but because of some of the incumbent advantages, I wouldn't say that it's
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predetermined which way you're going to go. Right. >> Right. you can actually do this quite
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quite quickly and I think you're showing the power of private capital can actually do this.
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>> So we've chatted a lot about this right? So one of the things when you think
00:15:23
about transforming a large enterprise what do you really need? You need a few pieces. One is you need data
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infrastructure that can ready you for the enterprise. You need the models adapted to you and then you actually
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need a new model for how the workforce is going to function because you have agents and humans and there's a massive
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change management exercise. So a lot of our partnership has been about sort of figuring out okay what is that new model
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going to be to transform these businesses and what does that mean when you get on the ground you look at
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department by department take HR how do you drive uh transformation of healthcare and how you take care of your
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people that process is horrible today and we have a business called transparent that goes and essentially
00:16:03
creates abundance in that regard which uses AI so you have direct access pentically to all kinds of healthcare
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services take yourself and then be able ble to be routed whether you need a surgery or you need to have cancer
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therapy or mental health and do it in a way that is seamless cost effective and helps uh enterprises take control of
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your cost structure you need coding to fundamentally transform that's what anthropic does there are companies
00:16:27
working on transforming the call centers there companies working on transforming
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your sales and marketing but then when you have these technologies in there how are the actual people going to do their
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work in concert with these agentic capabilities that is a whole new model and you know you guys are inventing a
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talk about that because there's a lot of in innovation that needs to happen in
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that that whole sort of workforce transformation that I think is ultimately where rubber is going to meet
00:16:50
the road on how quickly teams embrace it customers embrace it and we can actually
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diffuse AI into these businesses >> and Bob you've had to deal with this
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internally at your organization what's the right size and what happens when a
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piece of technology takes a career and takes out the first five years >> what happens to an organization when you
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just basically gut the first five years of development. And this is why some people in the economy are looking at AI
00:17:21
and they're scared >> and they're looking at AI and saying is this going to benefit me, my family, my
00:17:27
kids who are graduating from school, this technology, and I think management consulting is the perfect place to look
00:17:33
at it. Tell me honestly, the first couple of years you're training up one of these really smart kids to write up,
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you know, reports and do analysis that can be done with AI today perfectly, close to perfectly.
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>> Yeah. Um, so I'll give you a a couple stats first on us, then more general.
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Um, 25 squared and 40,000 and 25,000. What do I mean by that? So, let's look
00:18:01
at at McKenzie as a bit of a of an incubator. The 25 squared is we're simultaneously doing two things at the
00:18:07
same time. So we have client-f facing folks which most of you in the audience would know and think about when you
00:18:14
think about a Mackenzie consultant. We're growing that body at 25% next year. 25%. Unprecedented number of uh of
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of of new hires because the work is changing. They're not doing the stuff that you talked about. We saved we
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looked at it. We saved 1.5 million hours in search and synthesis last year, but we're dividending that to solve more
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complicated problems and do different things. You guys are probably sick of uh of McKenzie charts out there. You know,
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we have agents that do this. They just gave you 2.5 million of them in the last 6 months. I want to get rid of charts.
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But the the consultants are doing different things. We're adding 25% to that body.
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>> So, they're moving up the stack. >> They're moving up the stack and doing
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these more complicated problem. At the same time though about half our firm uh are nonclientf facing folks. We're down
00:19:03
25% in that group um with 10% increase in output. >> And so simultaneously and I know this is
00:19:11
hard also particularly for you know for folks to get we're going to be adding
00:19:15
and shrinking simultaneously with the two halves. >> And this has never happened in the
00:19:19
history of the firm. >> I've our model has always been synonymous that growth only occurs with
00:19:25
total headcount growth. Now it's actually splitting. We can grow in this part, the client facing side, and we can
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shrink in this part and have aggregate growth in total. And that's, you know,
00:19:35
that's a new paradigm and a new new dynamic. >> We're seeing this in venture. You and I
00:19:41
were around for the days where you'd give a team $3 million and they would come back in 18 months having spent it
00:19:48
on data centers and building a team of 20 people, 30 people. Then we'd see the
00:19:52
first version of the product 18 months later. And then the three billion now. >> Yeah, it's but it's it's insane just uh
00:20:00
how much more is getting done with less. And so do we worry about society and our
00:20:09
industry's ability to communicate to society this change? Because if you were
00:20:15
to tell an average business executive 10 years ago, prepare to hire 25% more people on this side of the business and
00:20:22
cut 25% on this side of the business. In the same 18month period, their head would explode.
00:20:26
>> Exactly. >> What? Why? How come? It doesn't make any sense. And then young people are
00:20:30
graduating and they're sending out a 100 200 resumes getting no job offers. We
00:20:37
were sitting here 10 years ago. every graduate from a decent school was like, I have an Uber, a Coinbase, and a Google
00:20:43
offer. Which one should I take for 150K? And those offers just aren't there. So,
00:20:49
how do we communicate better as an industry? And then what's the advice to young people coming into the workforce?
00:20:55
>> Yeah, look, um, every company in I was just say Silicon Valley, but broadly in
00:21:00
the tech industry, in the startup industry, what was it? Essentially, it looks like a C corp with a bunch of
00:21:04
engineers. But in a world where code self-writes, what what is that next level innovation?
00:21:10
What are these companies actually going to do? I think that's ultimately the
00:21:12
transition we're going through uh in what does innovation actually mean. It's
00:21:16
going to be less about being able to write code fat. It's much more going to
00:21:19
be about systemically how do we adopt this uh into the world and capabilities to your point about ambiguity in the
00:21:25
opening because we don't know about the capabilities of these technologies or
00:21:29
the how the world's shaping up. There's a lot of ambiguity. So to me the
00:21:33
companies that do really well and the the guide the way we guide the founders a lot of it is become iterative
00:21:40
constantly change constantly move forward as opposed to what used to be before was become precise find this
00:21:45
narrow edge create your growth loop and go build a company. Now it's like constantly iterate and in order to have
00:21:51
the customers give you the license to iterate comes down to trust and relationships. So founders that are very
00:21:58
good at engaging with customers, building trusting relationships and say, "Hey, we're going to go figure this out
00:22:03
together. We know how to leverage this technology, but we don't really know how
00:22:07
where it leads and what's what the possibilities are." Will we co-create?
00:22:10
And so it the advice I always give is it's all about radical collaboration. In
00:22:14
this next phase, we got to figure this out together where different stakeholders that all touch a system are
00:22:20
figuring out what this means to them and then what it means in terms of an overall optimization and the
00:22:25
transformation that we can do with it. you know, one maybe um exciting part to this because I I think you know, you
00:22:32
framed it as you're a graduate and how do you get into the workforce and is it
00:22:36
getting tougher and um we we did a little bit of work that said um what kind of skills are folks going to need
00:22:43
in an AI infused world, right? From an employer's point of view, less the the
00:22:47
startup, but you're more an atscale enterprise. What can the models not do? And so therefore, what skills will
00:22:53
humans play? And the work isn't done but but came back with with kind of three
00:22:59
key ideas. Um what can the models not do? Aspire, set the right aspiration. Do you go to low earth orbit? Do you go to
00:23:07
the moon? Do you go to Mars? That's a uniquely human capability. So how do you
00:23:12
look for the skills about aspiring and getting others to believe in the aspiration?
00:23:17
>> So leadership and goal setting >> human >> human >> judgment, right? And we've seen a lot
00:23:22
around in this room eval. But there's no right and wrong in these models. And so
00:23:28
how do you set the right parameters? The architecture um based on firm values, based on societal norms, whatever. How
00:23:35
do you build the skills to set what the right parameters are? And then finally, true creativity, right? The models are
00:23:43
inference models. The next most likely step, how do you think about orthogonal stuff? And so some of the work we've
00:23:49
been doing with large enterprises, if you believe in some of that, it can take you back to challenging some of your
00:23:55
assumptions on where you look for talent. It actually means that where you went to school matters a lot less. And
00:24:01
so, do you start looking for raw intrinsics? Can you widen the base? Can you actually look at, let's take a tech
00:24:08
background, not which university you graduated from, but what does your GitHub profile look like? Let's actually
00:24:14
get to the content. And could that actually start meaning that a wider set of people can enter the workforce with
00:24:20
different pathways? >> One of the things you said which really resonates is around creativity because
00:24:25
what when you know we were going to college it was all about learn how to solve problems really well right and and
00:24:32
and now in a world where we have this technology that can solve problems for us. It really is about asking the right
00:24:37
questions. It's like going back to that socratic dialogue. It is about creativity and who can imagine best what
00:24:43
the world's going to look like and then leverage these technologies to go shape
00:24:46
the world towards that. To your point about vision and and teaching our kids, you know, I get this question a lot
00:24:50
about what do you want how do you want your kids growing up? It's like learning
00:24:54
how to ask the right questions versus solving how to you know work on hard problems is a it's a very different
00:25:00
mindset and it is about curiosity and kind of back to being kids when you're
00:25:03
growing up. It is about challenging your curiosity. Can we actually rethink our pedagogy in a way that we can develop
00:25:10
this next generation to be more that than it's 8:00 on Wednesday morning and
00:25:14
I'm going to factor polinomials cuz I'm in you know 7th grade which is what our
00:25:18
system looks like today. >> Yeah. The the advice I've been giving to young people is there's nobody coming
00:25:25
for you. There's no training program. You have to make that for yourself >> and do not go in through the front door
00:25:32
with a resume. just email the CEO of the company >> and redesign their landing page and say,
00:25:38
"These are the three things that I think could be better and I saw you speak on
00:25:41
this podcast. I think your company's incredible. I would love to come work there and I did this spec work." Now,
00:25:46
people are like, "Why should I do free work to get a job to prove you actually
00:25:51
have a skill that is meaningful?" You're not going to be able to get into a
00:25:55
training program. So many folks now in corporate America, especially the the people who are onboarding people are
00:26:01
just like hiring somebody and training them is going to take longer than building an agent. I can build an agent.
00:26:08
Young people coming into the workforce I have to train are annoying. Setting up an agent who just does the
00:26:15
work is easy. That's the game on the field right now that people don't want
00:26:18
to talk about. Which means to stand out, you're going to have to show hootsp.
00:26:22
You're going to have to show drive. You're going to have to show passion.
00:26:25
And what college is doing that? What college is teaching that? What course is that?
00:26:29
>> We look, I think there's a massive gap in resilience. Yes. You know,
00:26:34
resilience. Because what you've got under that is you're going to get knocked down.
00:26:37
>> Yeah. Right. The question is, do you get back up? >> And how do you get back up? And I think
00:26:42
the the educational system today doesn't necessarily build institutional or individual capability in resilience. If
00:26:49
we could wave a magic wand, just to go off on a complete tangent here, what should the education system look like in
00:26:55
2026? Because you're buying businesses, you have one in healthcare. That's one
00:27:01
of the three hardest businesses to make change in historically. The other two here in America that have the most
00:27:08
regulation, are the most expensive, and are the hardest, and that Americans are suffering under the most are housing and
00:27:13
education. Those are the three big ones. When I run for president, that's going
00:27:17
to be my platform. is those three. I'm going to solve those three. But go ahead and solve education
00:27:22
for us right now. And are you going to buy a college next >> transform? Basically buying business all the
00:27:28
businesses that make no money. Is that where we're going? >> Um I I would say
00:27:32
>> oh the businesses that are the most >> Yeah. But the ones that need to endure
00:27:36
for the longest actually. That's the way I look at it. So here's the thing about
00:27:38
education. This idea that we spend 22 years learning and then we spend 40 years working is a broken idea. if if
00:27:45
the the learning of technology and the development of technology is going to be so dynamic. So what about going from a
00:27:52
four-year college to a lifelong college which is actually your relationship with
00:27:56
learning is that it's a lifelong skilling and reskilling outcome experience. We've talked about this
00:28:00
before as well. I mean there's there's there's some innovative college
00:28:03
presidents that are thinking about that which is first of all better business better lifetime value if you're a
00:28:09
college and you actually have a a client or a student you know for perpetuity versus paying you for four years. and
00:28:15
much more um useful for us to be able to go and have that capability and constantly learn what these cap
00:28:22
technologies are doing and how the workforce is evolving and how to stay ahead in terms of where the opportunity
00:28:27
is. This so learning has to become much more fluid and we need to become a community of lifelong learners uh as as
00:28:34
we adapt to a world where AI is being diffusing you know through us over the years. And I would just add uh I'm I'm
00:28:42
with you on this and you know that the system built close to 700 years ago was designed around a high fixed cost
00:28:51
libraries and professors to then take you out for a a finite period of time to learn and then effectively you're set
00:28:58
off into the workforce. I if you start to think about the half-life of skills getting shorter and shorter and we've
00:29:06
done some work at our global institute that said for an employer the return on investment that you give an employee in
00:29:12
terms of skills has shrunk by about half over the last 30 years used to be about
00:29:15
seven years return it's less than four years so about 3.6 six years now return
00:29:20
and that's only getting shorter and shorter as things change. So if you if you believe that I think you start to
00:29:26
pivot to are we teaching people to continue to learn new things as opposed to master a particular subject. Do you
00:29:33
have that ability? Um, one of the things that um, we've now indexed on and I
00:29:38
mentioned this 40,000 and 25,000 that is the number of humans we have and the number of personalized agents we have as
00:29:45
of last week in McKenzie and I think we'll be at parody before the end of uh,
00:29:49
by the end of this year. So you're literally deploying agents that can do a full 360 degree trusted job function.
00:29:57
>> Absolutely. >> Where is it working really well and where is it not working well?
00:30:02
It works when you have a specific um domain area that you know ultimately where value can be created. So for us
00:30:11
that's in structured problem solving. It's in around search and synthesis.
00:30:15
It's around commu more effective communication these types of of domain areas. But where I was going with this
00:30:22
so the skill is are you skilling people to actually become superhuman by leveraging agents.
00:30:28
>> Right. right? That becomes a skill. And I don't think we're actually equipping
00:30:32
that right now. It's a bit more random or sometimes actually excluded in the
00:30:38
classroom as opposed to embracing it and figuring out how do you actually take advantage of
00:30:41
>> it. It's almost like we're we need to train people to go from being part of
00:30:45
the orchestra to everybody being the conductor >> and everybody having their own orchestra
00:30:50
of agents working for them. And you know, I always look to startups because they're resource constrained. And I was
00:30:57
at a dinner in Singapore and I had a dozen founders there and I said, "Has anybody um you know hired anybody in the
00:31:05
last, you know, 60 days?" They all raised their hands and then I said, "Okay, um how many of you have an HR
00:31:12
person who wrote the job description?" Nobody raises hand. I said, "How many of
00:31:16
you typed into an LLM, write a job description for this?" All 12 hands go up. So now you think just HR the entire
00:31:26
blocking and tackling of it has been writing the job description and sorting through the resume. So then I asked the
00:31:31
next question which was how did you sort through the resumes coming in and they said half of them had built agents
00:31:37
>> Mhm. >> to sort through the resumes and stack rank them using AI and I said whoa holy
00:31:42
cow like this is like the typing pool the mail room the photo for those of you who are under 40 years old. We had a
00:31:50
room which was called the typing pool. Then we had one called the mail room where packages came in messengers all
00:31:55
those went away. that floor of the building got redeployed and I think that's what we're going to see like the
00:32:01
HR department the legal department all getting compressed really interesting >> it's already happening and I think as we
00:32:07
think about our own transformation for our own business we basically say every department needs to have AI teammates
00:32:12
now are those AI teammates like the co-pilot or pilot can you fully empower them to do stuff or are they giving you
00:32:18
efficiency that depends on how well the technology works how complex the problem
00:32:22
is how severe the problem is so like in healthcare for example if it's life and
00:32:26
death decisions, you want humans making those uh today because that technology isn't uh as reliable. So, so I think
00:32:34
sort of having a framework but saying every one of your departments is going to have these AI agents. Um, if you're
00:32:40
not doing that then you're not preparing yourself for this next phase and that's
00:32:43
that's a lot of what you're see you're already going to be one to one that's an
00:32:46
enormous uh ratio. >> Well, and but the problem I think Jason that you alluded to earlier in this is
00:32:51
there's all this potential um but folks aren't thinking through the dynamic
00:32:56
implications in their enterprise model versus the static. So the static might be hey there's all these departments I
00:33:04
can apply this I'll radically shrink it I'll reduce the number of layers in an
00:33:07
organization I may slow hiring on the inbound to your point the dynamic is okay but what does your company look
00:33:14
like in five years time >> and what I also often ask a CEO is okay you're doing all this stuff what's the
00:33:20
pathway to your job >> how does somebody get to your job in the org of the future you had a pathway it's
00:33:26
not going to be that same pathway but if you don't hire inbound folks You can't
00:33:30
continually laterally bring in a CEO. It's literally like taking the the bottom four rungs off the ladder to save
00:33:37
money today. >> And then everybody's jumping up trying to get in the organization. It's like,
00:33:41
well, we don't have a path there. You're going to have to be really thoughtful
00:33:43
about making that investment. And it feels like the first two years of AI were about cutting jobs and we really
00:33:50
need to think about, hey, it's not just about efficiency, it's about opportunity.
00:33:53
>> Exact. What's that other 25%. Right. That's what I think we got to lean into.
00:33:57
Let's take a little diversion here before I open my box. >> The black box,
00:34:01
>> my box here of all the great CES innovations over the last 20 years. Um,
00:34:08
physical AI. We've been talking here very cerebrally about what's happening
00:34:12
in enterprises, what's happening in software. But, um, you have self-driving
00:34:17
is probably the theme. I would I would dub 2026 CES as self-driving CES. I will dub 2027 as robotics, humanoid robotics
00:34:28
specifically. We're starting, obviously people are showing off all these incredible robots here, but I think
00:34:33
consumers will be experiencing them in 27, but consumers are experiencing this year self-driving. Neuro and Lucid have
00:34:41
an incredible product. Zuks has been here. Obviously, Elon's doing great things with Roboaxi.
00:34:47
Feels like he's closing in on a solution and getting very close. Whimo obviously
00:34:51
is leading the pack but then you also have BU Libaba we write Pony AI this is a global race what will the world look
00:34:59
like in 2026 in terms of self-driving and then any second and third order impacts of those and then do the same
00:35:06
for robotics >> if you go around the world today right you you go to Europe you go to the
00:35:11
Middle East where um you know there's a there is focus on interesting luxury
00:35:17
products there's a market for it BYD and a lot of these Chinese companies are
00:35:20
actually penetrating deeply everywhere because these these cap these companies have all the features and functionality
00:35:26
and they're really low cost and so one thing is that the the the dynamic of the
00:35:31
auto industry and European auto makers are all very dejected because they don't
00:35:35
know how they're going to compete with Chinese industry. US has innovation self-driving be innovation which allows
00:35:41
you to say the next generation of winning automotive companies um will take advantage of this platform shift.
00:35:47
US has a technology but it doesn't have the manufacturing uh capabilities to
00:35:52
actually say can you actually make it as cost- effectively as uh a Chinese maker
00:35:56
is going to be be. So it's not it's not as easy to figure out how the the world
00:36:00
order around automotive is going to re shift around the world. uh and and so part of the physical AI and the use of
00:36:07
AI in manufacturing is to figure out how do you design and manufacture um products next generation products right
00:36:14
here uh in in the US in a way that mimics the cost advantages of of China so that then our innovation can then
00:36:23
carry the data for us to be the global leaders yet again in this next phase so we have a company rebuild manufacturing
00:36:28
that's focusing on this for example there's a lot of focus that needs to get
00:36:31
on on that because if it's self-driving and it's not cost effective. Yeah, some
00:36:36
of us will buy it, but it's never going to be a mainstream product because cost
00:36:39
has I mean there's a reserve price that really shifts the demand patterns around
00:36:44
automotive and you probably have good data on this as well. We should talk about that. But we we got to get the AI
00:36:50
right and we got to get the manufacturing cost right as well. >> No, I think that there's a massive
00:36:55
coming down the cost curve on this. I'm with you, Jason. I think we're going to
00:36:58
see literally over the next 12 to 24 months a massive transformation. I think the race is a foot, right? The race is a
00:37:06
foot between a let's say a western stack and a and a Chinese stack on this and
00:37:11
then in rest of world it'll be interesting as a battleground to see where that plays out. But and you and I
00:37:17
were were talking a little bit about this. I think that is a massive trend. I think a larger trend will be the trend
00:37:24
um to robotics and and not just for human interaction but in manufacturing and and when you think about the
00:37:32
challenges that um the western world faces. So take the US. I was talking to the CEO of one of the large contract
00:37:39
manufacturers and she has 50,000 job openings right now for US manufacturing jobs in America that she can't fill.
00:37:46
>> Right? And our demographics aren't getting better on this front. Germany is
00:37:50
even worse situation. Korea, >> Germany, >> like another level. >> Yeah.
00:37:54
>> And I think the only way that you build resilient supply chains at the cost
00:37:59
point that you're talking about is it's going to be robotics at the heart. And
00:38:03
and this race, I think, is wide open. Korea leads the way in robots per per worker, right? They're about 1 to 10
00:38:10
right now. Germany and China are tied at second and the US then is a distant third. And so there's a real race. You
00:38:18
talked about the autonomy thing. I would actually jump to the robotics thing and
00:38:21
wonder how do I >> one of the issues in robotics is so so when you build the LLMs you could dump
00:38:26
them in the cloud experiment with something called chat GPT and becomes pervasive. If you have good robotics
00:38:31
models what's next? You don't have a hardware um capability uh that's like an
00:38:38
API infrastructure that diffuses those models fast. So like there's a lot that
00:38:41
needs to get built. So I actually think robotics will be slower than people think in terms of really taking hold.
00:38:46
But it's essential to go lead in that if you're going to lead in manufacturing
00:38:50
and therefore have that core advantage to play up the stack in industries like automotive. There's no other way to do
00:38:56
it. >> Yeah. I don't want to I don't want to name drop but I went two weeks two
00:39:00
Sundays ago I went to Tesla with Elon and I went and visited the Optimus lab. There were a large number of people
00:39:09
working on a Sunday at 10 a.m. >> Yeah. >> And I saw Optimus 3. I can tell you now,
00:39:14
nobody will remember that Tesla ever made a car. They will only remember the Optimus and that he is going to make a
00:39:20
billion of those. And it is going to be the most transformative technology product ever made in the history of
00:39:27
humanity because what LLMs are going to enable those products to do is understand the world
00:39:34
and then do things in the world that we don't want to do. >> Yeah. >> I I believe it'll be a onetoone ratio of
00:39:40
humans to optimists. And I think he's already won, but I don't want to speak
00:39:43
out of school. But I do have a box. We go to the box. >> I have a box. And these are all really interesting
00:39:51
technologies that we all got to see. How many people owned one of these? I mean,
00:40:00
Michael Douglas made this famous. Remember Wall Street on the beach making trades? And there was an amazing You
00:40:05
Will commercial. Remember the AT&T You Will commercial? And this was one of them. You'll be able to work remote from
00:40:12
the beach. What is the equivalent of this today? What is the equivalent of this today?
00:40:18
What do you think, you know, we're going to look back on this year and laugh at
00:40:23
in 30 years? This is something from the 80s, so I guess this is 30 years ago. What are we going to look at that we're
00:40:30
all enamored with today that we'll kind of get a little gau out of? >> Well, you know what? I'll I'll tell you,
00:40:36
by the way, I love it. It says California mobile phone on this. That was like the brand associated. And two
00:40:42
im two memories come to mind for me on this. One was Envy >> because when I started only the most
00:40:47
senior people could get one of these and I couldn't, right? Like I want they're
00:40:52
just like when you grabbed like I want one of those. $4 a minute. What was three or$4 dollars a minute?
00:40:57
>> And some battery lasts about 30 minutes. >> Exactly. Some new associate doesn't get
00:41:00
one of these. >> Did you when did you have your first mobile phone? >> I'm too young for this.
00:41:04
>> Too young for that. You're such a liar. You had the star tag like me. Had star
00:41:08
tag like me. >> But the uh but the second and this was made infamous was one of the great
00:41:12
things unfortunately great failures that we had was we did a project and it was it was published a while ago for AT&T in
00:41:19
the mid80s that said these things are never going to take off. >> Cell phones we're going to really get
00:41:23
going on the >> I don't know why you burned me with this one. >> By the way, let me remind like something
00:41:27
today just to answer your question. >> Think about like the a lot of the eyeglass innovation that's happening.
00:41:32
This was with your ears. the innovation we're trying to with the eyes on how to
00:41:35
intelligently navigate I think that there's so many attempts that have not worked in the last year or something.
00:41:39
There we go. >> All right. >> It's a really good segue because here's
00:41:42
the Google. >> Now, as ridiculous as I look right now and I can hear the cameras taking my
00:41:48
picture >> and you will not be spared because you'll be wearing them as well. Um I
00:41:52
remember when Larry and Sergey started walking around with these. >> In fact, Larry, uh I was at a party and
00:41:58
he came on the dance floor with these and I said, "Larry, take those off. All
00:42:01
the girls are gonna stop dancing if you keep walking around with them. He goes, "Really?" I was like, "Yeah, that's not
00:42:06
how dancing works." You don't. But if you think about this product, why did
00:42:09
they stop making this? They should have kept iterating. >> And this was AR before AR. You you see
00:42:16
right through it. >> Yeah. Ahead of its time, right? And >> go ahead and try it out, Bob. There you
00:42:21
go. >> And now forever you will also be in infamy. >> There you go. >> Your turn.
00:42:29
smart to do it. But >> all right, I'll do it. I'll But but by the way, the new ones aren't much
00:42:32
better. The form factor is better, but the utility isn't there. >> So when you look at it,
00:42:37
>> this this the today's version of this is what this was, I >> now here's one. This is a miniature
00:42:44
version. I tried to get this and if anybody can get me this, I I'll pay $10,000 for it. Uh maybe $25,000.
00:42:52
The Theronos one drop blood machine. This was like one of their chachkis. Ooh. >> But in in truth, you're now in
00:42:59
healthcare. This may have been a fraud allegedly. In reality, she's in jail, I guess. So,
00:43:06
I don't want to I mean, maybe there's a chance it was all she's innocent. Um,
00:43:11
who knows? Um, I'll leave that possibility out there. Um, allegedly. Um, but this the promise of this
00:43:20
captured people's imagination. A small amount of blood to get a lot of data back. And in fact, in fairness to
00:43:26
Elizabeth, she was able to do a couple of interesting tests with a small amount. This was a great product idea.
00:43:33
Correct. >> Yes. Yes. >> Will somebody create that with AI in the next 10 years?
00:43:38
>> Uh I think it's very likely because the the the challenge with this is how can
00:43:42
you actually manufacture those nano devices where you can take really low volumes and be accurate and measure
00:43:47
these things. Technology wasn't there. So when you going back to our hardware
00:43:50
manufacturing innovations, I think they will catch on to enable this and and you
00:43:54
want this you you want this to be that uh you know you can have real-time diagnostics think about a modern
00:44:00
physical and be much more preemptive about healthcare like pervasive effective capabilities like this these
00:44:06
endpoints will be useful for that >> and you have function health you have
00:44:08
superpower now doing I don't know if you guys use either of those products >> but getting your blood work done every
00:44:14
year having you know a concierge talk to you about it for butt 800 $100 a year, $600 a year. Obviously, consumer-ledd
00:44:21
healthcare and the Theronos vision. I >> I think there's a growing movement
00:44:24
around longevity. It's like become a cultural phenomenon. And so that's first
00:44:29
of all, the fact that consumers have propensity to pay. We have a company called RO for example that focus on
00:44:33
GLP1s because there's that propensity, it drives innovation to create more uh
00:44:38
products like this that are focused on keeping you healthy. >> How many people owned one of these?
00:44:43
Raise your hand. All right. And how many of people have three of these in their closet that they can't throw away?
00:44:49
>> I mean >> the keyboard. >> This was the greatest product ever.
00:44:52
>> So we we so I did a startup out of college. One of the very first apps that
00:44:56
was non- email on this. We wrote that and it was a merch merchandising app for Red Bull. So they could actually do
00:45:02
inventory tracking in a store. And this was like this is amazing product. You know the
00:45:06
>> I'm I'm still faster on this keyboard, >> right? I mean this was like for McKenzie
00:45:12
this was your cocaine. >> We we had some this was and we had some very senior people even when we might
00:45:18
that wouldn't give up. >> I have to tell you a story. It just gives me anxiety. I used to I I grew up
00:45:24
writing apps on this and then in 2011 I moved to the valley and I had my Blackberry. I put on a table like this.
00:45:31
I met with somebody who was a well-known person in the valley. We had a good conversation. At the end of it he said
00:45:36
you still use a Blackberry. I was like yeah. He's like, "Stop doing that. You
00:45:40
were judged in this meeting." >> I kid you not. Like, okay. >> Well, I mean, just think about
00:45:45
>> I don't want to touch it. You were uh you were holding it away from me.
00:45:49
>> Just think about how many carpal tunnel surgeries this created. >> Oh, absolutely.
00:45:53
>> I mean, this was great for the economy. >> This is an interesting one. How many
00:45:56
people owned >> Palm >> a pilot? Yeah. >> How many people own one of these?
00:46:01
>> Incredible, right? And it this one I have a stylus antenna. >> No, the stylus isn't here. We got this
00:46:06
off of eBay thanks to my friends at CES. Um, but you got to learn script and you
00:46:11
would be very good at, you know, spending at a party three or four minutes typing in some
00:46:15
>> and you'd have to have your phone separately, right? These are two different devices. So,
00:46:18
>> and if you really wanted to be like have a lot of swagger and a lot of RZ, you
00:46:23
would have this on one side of your belt. I know you had this problem. You did have, didn't you?
00:46:28
>> You have to be equal. >> And the Blackberry >> on the other side.
00:46:30
>> That was like you were like a gunslinger. >> Yeah. And then in the early days when
00:46:33
the Blackberry didn't have the phone, then you had the phone, too. So then you
00:46:36
you look like a utility guy. >> Hey Mont, I know that in college you lost a lot of brain cells to this one.
00:46:44
>> The first ad on the internet >> was a banner ad for Zema. >> Oh boy.
00:46:50
>> How many people have had a Zema? >> Oh, too many. >> These are headaches.
00:46:54
>> This was the most repulsive drink in the world. We got a empty can of it. Um,
00:46:59
it's still available, I think, in Sweden. I think there's one place that
00:47:03
still has the license and produces this horrific beverage. >> Um, >> but you know, you look at all the
00:47:09
carbonated stuff now, like >> I mean, I think version of this >> White Claw is there.
00:47:14
>> Yeah, I think that's that generation's >> You want No thanks.
00:47:20
>> I I actually ran a marathon with one of these on my waist in New York City. The
00:47:25
Sony Discman. It didn't skip when you were running. >> You see, this is a very good point. I
00:47:30
had the advanced one that had 10, >> it had a 10-second buffer. >> Oh,
00:47:36
>> this was elite at the time. It's an extra 50 bucks, but it would buffer 10
00:47:40
seconds. >> And then obviously the iPod came out. >> What What do we think in terms of the
00:47:47
limited capabilities, but the inspiration of this will we look back on at this moment in
00:47:53
time? In other words, a device that could go a thousandx in its capability, but providing the same similar
00:48:02
functionality. This case, being able to have portable music. That's interesting
00:48:06
because, you know, you think of the Walkman before this, right, which was the cassette,
00:48:10
>> right? >> The wooden skip that was durable. >> Durable. >> Advance in technology and moving from
00:48:15
analog to digital, but less durable. >> Yeah. But fidelity, >> better fidelity. Transition to iPod,
00:48:21
whatever. Right. that then solve both of the the equations. And you know, it gets
00:48:25
you think what what are the transition technologies we're in right now? And one
00:48:29
of the places I come back to is is um health wearables. Um so many different health wearables out there and they're
00:48:36
all attacking the problem from slightly different angles. >> Yes, >> some advances, but I think we're on the
00:48:41
cusp. I go back to marrying this plus wearables to having more continuous monitoring and data. We might be this
00:48:49
might be the transition step on uh >> between your eight sleep, your aura,
00:48:53
your whoop, all of that, your blood work coming together and giving you customized medicine.
00:48:58
>> I think that's a better answer than I was going to give, but my answer is the
00:49:02
LLM hallucinations >> because when you think about the intelligence, it's actually unreliable
00:49:07
in a lot of ways >> just like the music was unreliable with this and is that going to change
00:49:11
fundamentally over the next one. This was a very interesting device because for people who don't know, this
00:49:19
one might have text messaging on it, but it used to just tell you the phone number of the person who text called
00:49:24
back. So now if you were dating and you were in the dating pool and you got that
00:49:28
text from that special number, you're like, "Oh, how many minutes before I
00:49:31
call back? I got to go find a pay phone and call back." But you used to be able
00:49:36
to give a number. So after you page somebody, you could put in a couple of digits code. So, we started to have our
00:49:42
own vernacular uh 411 or 911. You could append to your beep some numbers like maybe your location, etc., the street
00:49:54
number you were on, etc. Really an interesting product in how we never got to turn off work that
00:50:04
led to always on >> doom scrolling. the neverending nature of, you know, our commitment to work.
00:50:12
And in some ways now we're starting to see a reverse of that. People are buying
00:50:16
phones. I I understand a lot of millennials now are buying digital cameras so they can leave their phone at
00:50:21
home and they're getting flip phones >> so they've unbundled it. Really
00:50:25
interesting. Any memories of the uh pager for you? >> Yeah. Well, first of all, you know, they
00:50:29
always say all the money is made in bundling and unbundling and that is happening. And I think it is about if
00:50:34
you're going to say the equivalent of this which is about how do we go back to
00:50:37
human connection uh and engaging in person as opposed to trying to you know be lonely online being fulfilled
00:50:44
offline. That's probably the behavioral change that's going to happen. What what
00:50:48
enables that I think is probably there is some social engineering that's going
00:50:50
to drive that. >> This has been an amazing hour. Well done gentlemen. Big round of applause for our
00:50:55
guest. >> Thank you so much for hosting us. This is incredible. Thank you. been a great
00:51:00
audience. Thank you for